Hierarchical machine learning model predicts antimicrobial peptide activity against Staphylococcus aureus

Introduction:Staphylococcus aureus is a dangerous pathogen which causes a vast selection of infections. Antimicrobial peptides have been demonstrated as a new hope for developing antibiotic agents against multi-drug-resistant bacteria such as S. aureus. Yet, most studies on developing classification tools for antimicrobial peptide activities do not focus on any specific species, and therefore, their applications are limited.Methods: Here, by using an up-to-date dataset, we have developed a hierarchical machine learning model for classifying peptides with antimicrobial activity against S. aureus. The first-level model classifies peptides into AMPs and non-AMPs. The second-level model classifies AMPs into those active against S. aureus and those not active against this species.Results: Results from both classifiers demonstrate the effectiveness of the hierarchical approach. A comprehensive set of physicochemical and linguistic-based features has been used, and after feature selection steps, only some physicochemical properties were selected. The final model showed the F1-score of 0.80, recall of 0.86, balanced accuracy of 0.80, and specificity of 0.73 on the test set.Discussion: The susceptibility to a single AMP is highly varied among different target species. Therefore, it cannot be concluded that AMP candidates suggested by AMP/non-AMP classifiers are able to show suitable activity against a specific species. Here, we addressed this issue by creating a hierarchical machine learning model which can be used in practical applications for extracting potential antimicrobial peptides against S. aureus from peptide libraries

EVALUATION OF THE INSECTICIDAL ACTIVITIES OF THREE EUCALYPTUS SPECIES CULTIVATED IN IRAN, AGAINST HYPHANTRIA CUNEA DRURY (LEPIDOPTERA: ARCTIIDAE)

In the current study, the larvicidal activity of leaf essential oils from three eucalyptus species (Eucalyptus largiflorens Meull, Eucalyptus oleosa Meull, and Eucalyptus spathulata Hook) against American white moth, Hyphantria cunea Drury 1773 (Lepidoptera: Arctiidae), was investigated. Mortality was recorded daily for three days after treatment. Leaf disc bioassays revealed that all three oils had strong insecticidal activity on the experimental insects insofar as 50% lethal concentrations (LC50) for E. oleosa, E. spathulata, and E. largiflorens at 24 h exposure time were 0.36, 0.61, and 1.24%, respectively. The time needed to kill 50% (LT50) values were calculated as 9.09 h with E. largiflorens, 11.03 h with E. oleosa, and 13.03 h with E. spathulata at the highest concentrations (2.5% for E. largiflorens, 2% for E. oleosa, and 2.5% for E. spathulata). Based on probit analysis, an increase in the susceptibility of the insect was associated with an increase in the different concentrations of all oils and the increase in the time of exposure. The results of this study show that leaf essential oils of E. largiflorens, E. oleosa, and E. spathulata might be considered as a potent source for the production of fine natural larvicides

An efficient electrochemical sensor based on CeVO4-CuWO4 nanocomposite for methyldopa

A novel modified electrode based on cerium vanadate and copper tungstate (CeVO _4 -CuWO _4 ) nanocomposite was prepared as a sensitive sensor for the methyldopa. The prepared nanocomposite was characterized by x-ray diffraction (XRD), energy dispersive x-ray spectroscope (EDX), Fourier transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM) methods. The cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques were applied for the evaluation of the electrochemical performance of the sensor. The enhanced active surface area, electro-catalytic activity, and expedient conductivity provided by the CeVO _4 -CuWO _4 nanocomposite led to the peak current increment with a well-resolved anodic peak for methyldopa in the presence of potential interferences. The CeVO _4 -CuWO _4 nanocomposite-based modified electrode successfully measured methyldopa over a wide concentration range of 0.02–400 μ M with the low limit of detection (LOD) of 0.006 μ M. The findings of the methyldopa sensing in human serum samples verified the proper efficiency of the proposed sensor

Facile and Effective Synthesis of Praseodymium Tungstate Nanoparticles through an Optimized Procedure and Investigation of Photocatalytic Activity

Regarding the importance of nanoparticles in today’s world, and in the light of the fact that their preparation can be a rather difficult task, we focused on the applicability of a simplistic direct precipitation approach for the preparation of praseodymium tungstate nanoparticles. To maximize the effectiveness of the method, a Taguchi robust design approach was applied to optimize the reaction in terms of the operating conditions influencing its outcome and the results were monitored by characterization of the Pr2(WO4)3 nanoparticles. Among the four parameters studied we found the dimensions of the produced nanoparticles to be determined by the concentrations of Pr3+ and WO43− solutions and the reaction temperature, while the flow rate of adding the cation solutions to the anion solution was found to leave very negligible effects on the product characteristics. To confirm the effect of the optimizations on the outcomes of the reaction, SEM, TEM, EDX, XRD, FT-IR and UV-Vis structural and morphological characterizations of the products were performed, the results of which were in agreement with those statistically predicted in the optimization procedure. Furthermore, as-synthesized praseodymium tungstate nanoparticles under ultraviolet light exhibited an efficient photocatalyst property in the photocatalytic degradation of methylene blue